Impact of the State of Charge Estimation on Model Predictive Control Performance in a Plug-In Hybrid Electric Vehicle Accounting for Equivalent Fuel Consumption and Battery Capacity Fade

Sockeel, Nicolas; Shahverdi, Masood; Mazzola, Michael S.

doi:10.1109/itec.2019.8790581

Cited by 9 publications

(8 citation statements)

References 28 publications

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“…It can manage functions including state of charge (SOC) monitoring, low voltage protection, high voltage protection, and over current and over temperature protection. As reported by Sockeel et al [22], equivalent fuel consumption and battery aging can be accounted by SOC estimation, which can be included in our future study.…”

Section: Internal Combustion Engine With Continuously Variable Transmmentioning

confidence: 92%

Development of a Hybrid Electric Motorcycle that Accords Energy Efficiency and Controllability via an Inverse Differential Gear and Power Mode Switching Control

et al. 2019

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In this study, inverse differential gear and power mode switching control were used to develop a hybrid electric motorcycle (HEM). An inverse differential gear power splitter was installed to integrate or distribute the power of an internal combustion engine (ICE), thus achieving single/dual power output. In addition, the transmission system was configured with continuously variable transmission to adjust the transmission speed reduction ratio and stabilize the power output. As a result, three power modes (i.e., the motor drive mode, ICE drive/generator mode, and dual power drive mode) could be switched between each other smoothly. Finally, our HEM was tested by a chassis power gauge. The test results showed that the HEM consumed 41.1% less fuel and produced 58.6% less exhaust emissions compared with conventional ICE motorcycles. In terms of controllability, the acceleration time for 0–100 m was 2.4 s less than the Taiwan E-scooter Standard (TES). The fastest highest vehicle speed was 2.1 times greater than the test of the TES.

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Section: Internal Combustion Engine With Continuously Variable Transmmentioning

confidence: 92%

Development of a Hybrid Electric Motorcycle that Accords Energy Efficiency and Controllability via an Inverse Differential Gear and Power Mode Switching Control

et al. 2019

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“…In order to facilitate online looking-up table, SOC is discretized as [0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9]. As the range of power demand of the studied PHEV is 0 kW to 50 kW and usually locates in 0 kW to 30 kW, power demand is discretized as [0, 2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,35,40,45,50]. Thus, in the PSO algorithm, the swarm is defined as X 1 = θ swarm , X 2 = λ swarm , and the initialization swarm θ 0 swarm and λ 0 swarm are denoted as two [20 × 13 × N]-dimensional tensors.…”

Section: Control Parameters Optimization Based On Pso-pmpmentioning

confidence: 99%

“…[23] presented a model predictive control (MPC) strategy and analyzed the Pareto optimal front of the cost function comprised by the equivalent fuel consumption and battery capacity fade during the charge sustaining mode of the battery. [24] further provided the impact of the estimated SOC by the battery management system on the performance of MPC. [25] studied the nonlinear model predictive control for the energy management of a power-split hybrid electric vehicle (HEV) to improve battery aging while maintaining the fuel economy at a reasonable level.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging

2020

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A hybrid electric vehicle (HEV) is a product that can greatly alleviate problems related to the energy crisis and environmental pollution. However, replacing such a battery will increase the cost of usage before the end of the life of a HEV. Thus, research on the multi-objective energy management control problem, which aims to not only minimize the gasoline consumption and consumed electricity but also prolong battery life, is necessary and challenging for HEV. This paper presents an adaptive equivalent consumption minimization strategy based on a recurrent neural network (RNN-A-ECMS) to solve the multi-objective optimal control problem for a plug-in HEV (PHEV). The two objectives of energy consumption and battery loss are balanced in the cost function by a weighting factor that changes in real time with the operating mode and current state of the vehicle. The near-global optimality of the energy management control is guaranteed by the equivalent factor (EF) in the designed A-ECMS. As the determined EF is dependent on the optimal co-state of the Pontryagin’s minimum principle (PMP), which results in the online ECMS being regarded as a realization of PMP-based global optimization during the whole driving cycle. The time-varying weight factor and the co-state of the PMP are map tables on the state of charge (SOC) of the battery and power demand, which are established offline by the particle swarm optimization (PSO) algorithm and real historical traffic data. In addition to the mappings of the weight factor and the major component of the EF linked to the optimal co-state of the PMP, the real-time performance of the energy management control is also guaranteed by the tuning component of the EF of A-ECMS resulting from the Proportional plus Integral (PI) control on the deviation between the battery SOC and the optimal trajectory of the SOC obtained by the Recurrent Neural Network (RNN). The RNN is trained offline by the SOC trajectory optimized by dynamic programming (DP) utilizing the historical traffic data. Finally, the effectiveness and the adaptability of the proposed RNN-A-ECMS are demonstrated on the test platform of plug-in hybrid electric vehicles based on GT-SUITE (a professional integrated simulation platform for engine/vehicle systems developed by Gamma Technologies of US company) compared with the existing strategy.

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“…In this research, the system models are formulated as Mixed-Integer Linear Programming (MILP) problems. The MPC method has also been adopted for battery management in Hybrid Electric Vehicles (HEV) in [25], [26], in which the MPC controller attempts to minimize the vehicle equivalent fuel consumption and the battery capacity fade at the same time. To enhance the battery lifespan and power performance, [27] and [28] propose an MPC approach to reduce the battery power variation and maintain supercapacitors' SOC at their desired values.…”

Section: Introductionmentioning

confidence: 99%

Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

Wang¹,

Atkin²,

Bazmohammadi³

et al. 2021

Preprint

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Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the robustness of the model. Besides, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.

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Impact of the State of Charge Estimation on Model Predictive Control Performance in a Plug-In Hybrid Electric Vehicle Accounting for Equivalent Fuel Consumption and Battery Capacity Fade

Cited by 9 publications

References 28 publications

Development of a Hybrid Electric Motorcycle that Accords Energy Efficiency and Controllability via an Inverse Differential Gear and Power Mode Switching Control

Development of a Hybrid Electric Motorcycle that Accords Energy Efficiency and Controllability via an Inverse Differential Gear and Power Mode Switching Control

Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging

Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

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